# load packages
library(tensorflow)
library(keras)
library(reticulate)
install_tensorflow()
#
# parameters
batch_size <- 32
epochs <- 20
data_augmentation <- TRUE
model1 %>%
layer_conv_2d(
filter = 32, kernel_size = c(3,3), padding = "same",
input_shape = c(32, 32, 3)
) %>%
layer_activation("relu") %>%
# Second hidden layer
layer_conv_2d(filter = 32, kernel_size = c(3,3)) %>%
layer_activation("relu") %>%
# Use max pooling
layer_max_pooling_2d(pool_size = c(2,2)) %>%
#layer_dropout(0.25) %>%
# 2 additional hidden 2D convolutional layers
layer_conv_2d(filter = 32, kernel_size = c(3,3), padding = "same") %>%
layer_activation("relu") %>%
layer_conv_2d(filter = 32, kernel_size = c(3,3)) %>%
layer_activation("relu") %>%
# Use max pooling once more
layer_max_pooling_2d(pool_size = c(2,2)) %>%
#layer_dropout(0.25) %>%
# Flatten max filtered output into feature vector
# and feed into dense layer
layer_flatten() %>%
layer_dense(512) %>%
layer_activation("relu") %>%
#layer_dropout(0.5) %>%
# Outputs from dense layer are projected onto 10 unit output layer
layer_dense(10) %>%
layer_activation("softmax")
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Input 0 of layer conv2d_11 is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 10]model1 without data augmentation
model1 %>% fit(
x_train, y_train,
batch_size = batch_size,
epochs = epochs,
validation_data = list(x_test, y_test),
shuffle = TRUE)